[1] I. Ahmed and G. Jeon, A real-time person tracking system based on SiamMask network for intelligent video
surveillance, J. Real-Time Image Process. 18 (2021), no. 5, 1803–1814.
[2] A.F. Al-Battal, Y. Gong, L. Xu, T. Morton, C. Du, Y. Bu, I.R. Lerman, R. Madhavan and T.Q. Nguyen, A CNN
segmentation-based approach to object detection and tracking in ultrasound scans with application to the vagus
nerve detection, Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS, 2021, pp. 3322–3327.
[3] D. Anitta, Human head pose estimation based on HF method, Microprocess. Microsyst. 82 (2021), 103802.
[4] M.R. Bachute and J.M. Subhedar, Autonomous driving architectures: insights of machine learning and deep
learning algorithms, Mach. Learn. Appl. 6 (2021), 100164.
[5] L. Bertinetto, J. Valmadre, J.F. Henriques, A. Vedaldi and P.H.S. Torr, Fully-convolutional siamese networks for
object tracking, Eur. Conf. Comput. Vis. Springer, Cham., 2016, pp. 850–865.
[6] A. Bochkovskiy, C.-Y. Wang and H.-Y. M. Liao, Yolov4: optimal speed and accuracy of object detection, arXiv
Prepr. arXiv2004.10934, (2020).
[7] J. Chen, C. Zhang, J. Luo, J. Xie and Y. Wan, Driving maneuvers prediction based autonomous driving control
by deep monte carlo tree search, IEEE Trans. Veh. Technol. 69 (2020), no. 7, 7146–7158.
[8] H.-K. Chiu, J. Li, R. Ambrus and J. Bohg, Probabilistic 3d multi-modal, multi-object tracking for autonomous
driving, IEEE Int. Conf. Robotics and Automation (ICRA), 2021, pp. 14227–14233.[9] G. Ciaparrone, F. Luque S´anchez, S. Tabik, L. Troiano, R. Tagliaferri and F. Herrera, Deep learning in video
multi-object tracking: a survey, Neurocomput. 381 (2020), 61–88.
[10] Y. Cui, R. Chen, W. Chu, L. Chen, D. Tian, Y. Li and D. Cao, Deep learning for image and point cloud fusion
in autonomous driving: a review, IEEE Trans. Intell. Transp. Syst. 23 (2022), no. 2, 722–739.
[11] Y. Deng, T. Zhang, G. Lou, X. Zheng, J. Jin and Q.L. Han, Deep learning-based autonomous driving systems: a
survey of attacks and defenses, IEEE Trans. Ind. Inf. 17 (2021), no. 12, 7897–7912.
[12] M. Everingham, L. Van Gool, C.K.I. Williams, J. Winn and A. Zisserman, The pascal visual object classes (voc)
challenge, Int. J. Comput. Vis. 88 (2010), no. 2, 303–338.
[13] H. Fujiyoshi, T. Hirakawa and T. Yamashita, Deep learning-based image recognition for autonomous driving,
IATSS Res. 43 (2019), no. 4, 244–252.
[14] K. He, G. Gkioxari, P. Doll and R. Girshick, Mask r-cnn, Proc. IEEE Int. Conf. Comput. Vis., 2017, pp. 2961–
2969.
[15] N. Ijaz and Y. Wang, Automatic steering angle and direction prediction for autonomous driving using deep learning, Proc. Int. Symp. Comput. Sci. Intell. Control. ISCSIC 2021, pp. 280–283.
[16] L. Kalake, W. Wan and L. Hou, Analysis based on recent deep learning approaches applied in real-time multi-object
tracking: a review, IEEE Access 9 (2021), 32650–32671.
[17] A. Kuznetsova, H. Rom, N. Alldrin, J. Uijlings, I. Krasin, J. Pont-Tuset, S. Kamali, S. Popov, M. Malloci and
T. Duerig, The open images dataset V4: unified image classification, object detection, and visual relationship
detection at scale, arXiv 2018. arXiv preprint arXiv:1811.00982, (2018).
[18] G. Li, Y. Yang, X. Qu, D. Cao and K. Li, A deep learning based image enhancement approach for autonomous
driving at night, Knowledge-Based Syst. 213 (2021), 106617.
[19] T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Doll´ar and C.L. Zitnick, Microsoft coco:
common objects in context, Eur. Conf. Comput. Vision, 2014, pp. 740–755.
[20] G. Litjens, T. Kooi, B.E. Bejnordi, A.A.A. Setio, F. Ciompi, M. Ghafoorian, J.A. Van Der Laa, B. Van Ginneken
and C.I. S´anchez, A survey on deep learning in medical image analysis, Med. Image Anal. 42 (2017), 60–88.
[21] W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.Y. Fu and A.C. Berg, Ssd: single shot multibox detector,
Eur. Conf. Comput. Vision, 2016, pp. 21–37.
[22] Y. Liu, P. Sun, N. Wergeles and Y. Shang, A survey and performance evaluation of deep learning methods for
small object detection, Expert Syst. Appl. 172 (2021), 114602.
[23] C. Liu, Y. Tao, J. Liang, K. Li and Y. Chen, Object detection based on YOLO network, Proc. 2018 IEEE 4th Inf.
Technol. Mechatronics Eng. Conf. ITOEC 2018, pp. 799–803.
[24] X. Ma, W. Ouyang, A. Simonelli and E. Ricci, 3d object detection from images for autonomous driving: a survey,
arXiv preprint arXiv:2202.02980, (2022), 1–26.
[25] A. Makandar, D. Mulimani and M. Jevoor, Preprocessing step–review of key frame extraction techniques for object
detection in video, Int. J. Curr. Eng. Technol. 5 (2015), no. 3, 2036–2039.
[26] K. Muhammad, A. Ullah, J. Lloret, J. Del Ser and V.H.C. De Albuquerque, Deep learning for safe autonomous
driving: current challenges and future directions, IEEE Trans. Intell. Transp. Syst. 22 (2021), no. 7, 4316–4336.
[27] M. Pervaiz, Y.Y. Ghadi, M. Gochoo, A. Jalal, S. Kamal and D.S. Kim, A smart surveillance system for people
counting and tracking using particle flow and modified som, Sustain. 13 (2021), no. 10, 1–20.
[28] G. Prabhakar, B. Kailath, S. Natarajan and R. Kumar, Obstacle detection and classification using deep learning
for tracking in high-speed autonomous driving, TENSYMP 2017 - IEEE Int. Symp. Technol. Smart Cities, 2017,
pp. 3–8.
[29] A. Raghunandan, P. Raghav and H.R. Aradhya, Object detection algorithms for video surveillance applications,
Proc. 2018 IEEE Int. Conf. Commun. Signal Process. ICCSP 2018, pp. 563–568.
[30] K. Ragland and P. Tharcis, A survey on object detection, classification and tracking methods, Int. J. Eng. Res.Technol. 3 (2014), no. 11, 622–628.
[31] J. Redmon and A. Farhadi, YOLO9000: better, faster, stronger, Proc. IEEE Int. Conf. Comput. Vis. Pattern
Recog., (2017), pp. 7263–7271.
[32] S. Ren, K. He, R. Girshick and J. Sun, Faster r-cnn: towards real-time object detection with region proposal
networks, Adv. Neural Inf. Process. Syst. 28 (2015).
[33] F. Rosique, P.J. Navarro, C. Fern´andez and A. Padilla, A systematic review of perception system and simulators
for autonomous vehicles research, Sensors 19 (2019), no. 3.
[34] L. Rupasinghe and M.C. Liyanapathirana, Human tracking and profiling for risk management, Global J. Comput.
Sci. Technol. 22 (2022), no. 1.
[35] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein
and A.C. Berg, Imagenet large scale visual recognition challenge, Int. J. Comput. Vis. 115 (2015), no. 3, 211–252.
[36] A. Shafique, G. Cao, Z. Khan, M. Asad and M. Aslam, Deep learning-based change detection in remote sensing
images: a review, Remote Sens. 14 (2022) , no. 4, 1–40.
[37] V. Sharma and R.N. Mir, A comprehensive and systematic look up into deep learning based object detection
techniques: a review, Comput. Sci. Rev. 38 (2020), 100301.
[38] K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition, 3rd Int. Conf.
Learn. Represent. ICLR 2015 - Conf. Track Proc. 2015, pp. 1–14.
[39] Z. Soleimanitaleb, M.A. Keyvanrad and A. Jafari, Object tracking methods: a review, 9th Int. Conf. Comput.
Knowl. Eng. ICCKE 2019, pp. 282–288.
[40] P. Sun, H. Kretzschmar, X. Dotiwalla, A. Chouard, V. Patnaik, P. Tsui, J. Guo, Y. Zhou, Y. Chai, B. Caine and
V. Vasudevan, Scalability in perception for autonomous driving: waymo open dataset, Proc. IEEE Comput. Soc.
Conf. Comput. Vis. Pattern Recognit. 2020, pp. 2443–2451.
[41] A. U¸car, Y. Demir and C. G¨uzeli¸s, Object recognition and detection with deep learning for autonomous driving
applications, Simulation 93 (2017), no. 9, 759–769.
[42] K.E.A. Van De Sande, J.R.R. Uijlings, T. Gevers and A.W.M. Smeulders, Segmentation as selective search for
object recognition, Proc. IEEE Int. Conf. Comput. Vis. 2011, no. 2, pp. 1879–1886.
[43] M. Waheed, M. Javeed and A. Jalal, A novel deep learning model for understanding two-person interactions using
depth sensors, Int. Conf. Innov. Comput. (ICIC), IEEE, 2022, 1–8.
[44] T. Wollmann and K. Rohr, Deep consensus network: aggregating predictions to improve object detection in microscopy images, Med. Image Anal. 70 (2021), 102019.
[45] Y. Yin, Design of deep learning based autonomous driving control algorithm, 2nd Int. Conf. Consumer Electron.
Comput. Engin. (ICCECE), IEEE. 2022, pp. 423–426.
[46] Z. Zhang, Y. Li, W. Wu, H. Chen, L. Cheng and S. Wang, Tumor detection using deep learning method in
automated breast ultrasound, Biomed. Signal Process. Control 68 (2021), 102677.
[47] H.Y. Zhou, C. Wang, H. Li, G. Wang, S. Zhang, W. Li and Y. Yu, SSMD: semi-supervised medical image detection
with adaptive consistency and heterogeneous perturbation, Med. Image Anal. 72 (2021), 102117.